Behavioral AI Evaluation & Deployment

This capstone unit consolidates prior learning to evaluate, justify, and plan real-world behavioral AI initiatives. Students learn to measure ROI through experimentation, map advanced ML and generative approaches to business needs, and address ethical, privacy, and operational considerations for deployment.

Learning Objectives

• Analyze experimental designs and results (A/B tests, lift, statistical significance) to calculate ROI and produce a data-driven recommendation report for a behavioral AI intervention
• Apply knowledge from classical ML, deep learning, and generative AI readings to select and justify appropriate model approaches for specific business problems, considering performance, interpretability, and resource constraints
• Evaluate ethical, privacy, and risk implications of proposed behavioral AI solutions and recommend mitigation strategies, compliance steps, and stakeholder communication plans
• Demonstrate synthesis of course notes and experimental findings by creating an actionable implementation plan with KPIs, monitoring strategies, and iteration guidance suitable for business stakeholder presentation